Research Data Management 101 Course Materials

Week 1

Objectives:

1. Describe how the data lifecycle fits into the larger research lifecycle.
2. Articulate the importance of RDM to the research lifecycle.
3. Summarize the potential roles of librarians in RDM

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 1. What is Data and Why Should Librarians Be Involved
• Chapter 2. Understanding Research and the Role of Data

NYU Compass Modules 2 (The Data Lifecycle) & 3 (Understanding Researchers)

Further Resources

• Khan, H. R., & Du, Y. (2017). What is a Data Librarian?: A Content Analysis of Job Advertisements for Data Librarians in the United States Academic Libraries.
  • This article discusses the various requirements and preferred qualifications that are seen in data librarian job postings. This can give a good overview of new content to learn, or develop your current role to include.
• Federer, L. (2018). Defining data librarianship: a survey of competencies, skills, and training. Journal of the Medical Library Association: JMLA, 106(3), 294.
  • Federer's article does a similar evaluation as the Khan and Du article but goes a step further to discuss what the people in data librarian roles are actually doing in their day to day work.
• Lesson 1. DataONE Education Modules.
  • Covers trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data lifecycle.
• The MLA Guide to Data Management for Librarians, edited by Lisa Federer, 2016
  • Chapter 1. Research Data Management for the Biomedical Digital Research Enterprise
  • Chapter 2. What Could Possibly Go Wrong?
• Data Management: A Practical Guide for Librarians by Margaret Henderson
  • Chapter 14. Data Management Roles for Librarians

Week 2

Objectives

1. Explain what data curation encompasses
2. Explain various types of data (e.g. surveys, video, images)
3. Identify which data elements are important to document
4. Recommend file naming convention based on best practices
5. Check a dataset for potential privacy issues

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 3. Best Practices for Working with Research Data
• Chapter 6. Documentation and Metadata

NYU Compass Module 5 (Data Documentation Best Practices)

Further Resources

• Data Curation Profile Toolkit
  • This toolkit was designed to help "launch discussions between librarians and faculty and ait in the planning of data services that directly address the needs of researchers."
• ICPSR Data Management & Curation
  • For a deeper dive into a data repository and how it manages and curates data
• The MLA Guide to Data Management for Librarians, edited by Lisa Federer, 2016
  • Chapter 3. Research Data as Record

Week 3

Objectives

1. Distinguish between standards, metadata, taxonomy, and ontology
2. Locate and choose appropriate metadata/descriptors/ontologies for a given dataset
3. Apply selected standards to a given dataset

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 6. Documentation and Metadata

OHSU BD2K Open Educational Resources Project (videos)

• Module 5: Basic Research Data Standards (12:55) covers what constitutes a data standard, why use one, which ones should be used, and how to choose one.
• Module 14.1: Controlled Vocabularies, Ontologies, and Data Linking (PPT) covers definitions of controlled vocabulary, taxonomy, thesaurus, and ontology, and highlights some use cases for ontologies.
• Module 8.2: Sources and Types of Clinical Data (25:31) gives a nice overview of what clinical data is and challenges of managing this type of data.
• Optional modules:
  • Module 15.1: Metadata Overview (11:04) provides a general overview of metadata and some best practices for creating and using metadata.
  • Module 15.2: Metadata Standards (13:16) provides an overview of metadata schemas, value standards, reporting guidelines, and encoding specifications.
  • Module 4.1: Clinical Data Standards Related to Big Data -- Standards and Interoperability (26:29) covers standards and interoperability specifically for clinical data.
  • Module 16: Terminology Standards will give you an overview of major terminology standards in biomedicine.
    • Part 1 (17:14) for an overview of terminology standards and ICD-9
    • Part 2 (18:47) for coverage of ICD-10 and RxNorm
    • Part 3 (23:42) for coverage of LOINC, SNOMED, and UMLS

Further Resources

• Lesson 7. DataONE Education Modules
  • This lesson covers definition of metadata, information included in metadata, selection of metadata standards, the value and utility of metadata.
• NYU Compass Module 6: Data Standards
  • At the end of the module, you'll be able to "recognize the value of using standards for research; locate standards for various biomedical disciplines; and distinguish between terminologies, reporting guidelines, and data models as standard types."
• BD2K Fundamentals of Data Science: Ontologies (10/7/2016)
  • Michel Dumontier from Stanford University talks about "the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions." He covers "the tools of the trade to design, find, and reuse ontologies" and discusses "applications of ontologies in the fields of diagnosis and drug discovery."
• Standards Resources:
  • CDISC Standards
  • Clinical Data Acquisition Standards Harmonization (CDASH)
  • Linked Open Vocabularies (LOV)
  • LOINC
  • Review: Interoperability standards by Susanna-Assunta Sansone and Philippe Rocca-Serra
  • RxNorm
  • SNOWMED-CT
  • Study Data Tabulation Model (SDTM)
  • The Open Biological and Biomedical Ontology (OBO) Foundry
  • Unified Medical Language System® (UMLS®)
  • Value Set Authority Center (VSAC)
• Metadata Resources:
  • Common Metadata Elements for Cataloging Biomedical Datasets by Kevin Read
  • DataCite Metadata Generator
  • DCC Disciplinary Metadata
  • NIH CDE Resource Portal
  • RDA Metadata Directory
• Ontology Resources:
  • BFOBasic Formal Ontology
  • BioPortal
  • FAIRsharing
  • Gene Ontology Consortium
  • Phenix
  • PhenomeCentral
  • PhenoTips

Week 4

Objectives

1. Evaluate preservation needs of a dataset (e.g. file format, software)
2. Identify appropriate data repositories for a given dataset
3. Discuss potential solutions for datasets with security/privacy issues (HIPAA)
4. Explain how policies affect data ownership, security, and storage

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 5. Storing, Curating, and Preserving Data

DataONE Lesson 10

OHSU BD2K Module 20

• Part 1 (28:54) focuses on concerns with privacy, including data breaches, tech challenges, and examples of re-identification.
• Part 2 (22:51) focuses on concerns with security, including leakage of data, consequences of medical identity theft, and some tools to protect health information.
• Part 3 (29:47) discusses the HIPAA Privacy Rule, including key privacy compliance areas, breaches, and penalties.
• Part 4 (14:34) discusses the HIPAA Security Rule, including requirements, safeguards, and challenges with mobile devices.

NYU Compass Module 7: Storage, Preservation, and Sharing

Further Resources

• Lesson 6. DataONE Education Modules.
  • This lesson covers backups, archives, and data preservation if you need a basic refresher on definitions. It also covers some recommended practices.
• NECDMC Module 5: Legal and Ethical Considerations for Research Data.
  • Alternative to the DataONE Module 10. This one mentions IRB but doesn't go into details.
• Recommended Data Repositories. Scientific Data.
  • See this page for some suggested repositories based on discipline.
• Health Information Privacy. U.S. Department of Health & Human Services.
  • Everything you need to know about HIPAA.
• bioCADDIE
• Data.gov
• ICPSR
• Re3data
• REDCap

Week 5

Objectives

1. Articulate the FAIR data principles
2. Explain the importance of research reproducibility
3. Describe the concept of "open data" and challenges for sharing biomedical research data
4. Explain data sharing incentives, data citations, and data journals

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 7. Publishing and Sharing Data

The FAIR Data Principles. FORCE11.

Wilkinson, M.D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, Article number: 160018 (2016): doi:10.1038/sdata.2016.18

Boeckhout, M., Zielhuis, G. A., & Bredenoord, A. L. (2018). The FAIR Guiding Principles for Data Stewardship: Fair Enough? European Journal of Human Genetics, 26(7), 931–936. http://doi.org/10.1038/s41431-018-0160-0

OHSU BD2K Lecture: "Why Data Sharing & Reuse are Hard to Do"

NYU Compass Module 4: Research Data Management Climate

Further Resources

• Lesson 2. DataONE Education Modules.
  • This lesson covers data sharing in the context of the data life cycle, the value of sharing data, concerns about sharing data, and methods and best practices for sharing data.
• Lesson 8. DataONE Education Modules.
  • This lesson covers data citation.
• The MLA Guide to Data Management for Librarians, edited by Lisa Federer, 2016.
  • Chapter 4. Raising Researchers' Awareness of Biomedical Data Journals to Promote Data Sharing
• DataCite
  • This site aims to help you locate, identify, and cite research data.
• Transparency and Openness Promotion (TOP) Guidelines
  • An important initiative to follow, over 5000 signatories from journals and organizations supporting transparency, open sharing, and reproducibility.

Week 6

Objectives

1. Explain DMP requirements of funding agencies (NIH, NSF)
2. Create a DMP that meets the requiremetns of a selected funding agency

Data Management: A Practical Guide for Librarians, Margaret Henderson

• Chapter 8. Writing Data Management Plans

NIH Data Sharing Policy

• Policy and Implementation Guidelines (2003)
• Frequently Asked Questions (updated 2004)
• Related Guidance (updated 2016), with particular attention to
  • Key Elements to Consider in Preparing a Data Sharing Plan under NIH Extramural Support (2009) -- closest to DMP guidance
  • Example Plan (2010) -- sample language to give you an idea of how to address the key elements
• Example from DMPTool

Lesson 3. DataONE Education Modules.

• Slides
• One page handout

Further Resources

• Sheehan, J. (2016, February 22). Increasing Access to the Results of Federally Funded Science. The White House Blog (President Barack Obama Archives).
  • Read this post for some history on the push for DMPs. If you haven't seen the 2013 OSTP memorandum, there's a link in the first sentence. There's also a link to a website that was tracking which funding agencies were in compliance -- don't know if it's been updated recently.
• The MLA Guide to Data Management for Librarians, edited by Lisa Federer, 2016
  • Chapter 7. Library Support for Data Management Plans
• ICPSR. Framework for Creating a Data Management Plan.
  • This is a great resource for creating a general DMP. Definitely keep this around for consultations.
• Digital Curation Centre. Data Management Plans.
  • This resource was developed in the UK for their funder requirements. They also have other great training resources.